CiteULike: jxl's library [79 articles]

An evaluation of selected global (Q)SARs/expert systems for the prediction of skin sensitisation potential

Patlewicz — 2007-12-06T19:46:08-00:00

SAR and QSAR in Environmental Research, Vol. 18, No. 5-6. (July 2007), pp. 515-541.

Causal inference in biomolecular pathways using a Bayesian network approach and an Implicit method

Ben — 2008-06-23T15:07:32-00:00

Journal of Theoretical Biology, Vol. In Press, Corrected Proof

We introduce here the concept of Implicit networks which provide, like Bayesian networks, a graphical modelling framework that encodes the joint probability distribution for a set of random variables within a directed acyclic graph. We show that Implicit networks, when used in conjunction with appropriate statistical techniques, are very attractive for their ability to understand and analyze biological data. Particularly, we consider here the use of Implicit networks for causal inference in biomolecular pathways. In such pathways, an Implicit network encodes dependencies among variables (proteins, genes), can be trained to learn causal relationships (regulation, interaction) between them and then used to predict the biological response given the status of some key proteins or genes in the network. We show that Implicit networks offer efficient methodologies for learning from observations without prior knowledge and thus provide a good alternative to classical inference in Bayesian networks when priors are missing. We illustrate our approach by an application to simulated data for a simplified signal transduction pathway of the epidermal growth factor receptor (EGFR) protein.

Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles

Guangtao Ge — 2008-06-12T08:03:25-00:00

BMC Bioinformatics, Vol. 9 (11 June 2008), 275.

A Fully Computational Model for Predicting Percutaneous Drug Absorption

D Neumann — 2006-11-01T16:02:53-00:00

J. Chem. Inf. Model., Vol. 46, No. 1. (23 January 2006), pp. 424-429.

A Comparison of Machine Learning Algorithms for Chemical Toxicity Classification Using a Simulated Multi-Scale Data Model

Richard Judson — 2008-05-20T15:47:58-00:00

BMC Bioinformatics, Vol. 9 (19 May 2008), 241.

Analyzing and visualizing expression data with Spotfire.

D Kaushal — 2008-04-25T08:25:24-00:00

Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.], Vol. Chapter 7 (October 2004)

This unit assumes the reader is familiar with the Spotfire environment, has successfully installed Spotfire, and has uploaded and prepared data for analysis. It presents numerous methods for analyzing microarray data. Specifically, the first two protocols describe methods for identifying differentially expressed genes via the t-test/ANOVA and the distinction calculation respectively. Another protocol discusses how to conduct a profile search. Additional protocols illustrate various clustering methods, such as hierarchical clustering, K-means clustering, and principal components analysis. A protocol explaining coincidence testing allows the reader to compare the results from multiple clustering methods. Additional protocols demonstrate querying the Internet for information based on the microarray data, mathematically transforming data within Spotfire to generate new data columns, and exporting Spotfire visualizations.

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

Wouter Meuleman — 2008-02-08T01:10:44-00:00

BMC Bioinformatics, Vol. 9 (07 February 2008), 88.

Weight of Evidence: A Review of Concept and Methods

Douglas Weed — 2006-11-09T19:41:22-00:00

Risk Analysis, Vol. 25, No. 6. (2005), pp. 1545-1557.

"Weight of evidence" (WOE) is a common term in the published scientific and policy-making literature, most often seen in the context of risk assessment (RA). Its definition, however, is unclear. A systematic review of the scientific literature was undertaken to characterize the concept. For the years 1994 through 2004, PubMed was searched for publications in which "weight of evidence" appeared in the abstract and/or title. Of the 276 papers that met these criteria, 92 were selected for review: 71 papers published in 2003 and 2004 (WOE appeared in abstract/title) and 21 from 1994 through 2002 (WOE appeared in title). WOE has three characteristic uses in this literature: (1) metaphorical, where WOE refers to a collection of studies or to an unspecified methodological approach; (2) methodological, where WOE points to established interpretative methodologies (e.g., systematic narrative review, meta-analysis, causal criteria, and/or quality criteria for toxicological studies) or where WOE means that "all" rather than some subset of the evidence is examined, or rarely, where WOE points to methods using quantitative weights for evidence; and (3) theoretical, where WOE serves as a label for a conceptual framework. Several problems are identified: the frequent lack of definition of the term "weight of evidence," multiple uses of the term and a lack of consensus about its meaning, and the many different kinds of weights, both qualitative and quantitative, which can be used in RA. A practical recommendation emerges: the WOE concept and its associated methods should be fully described when used. A research agenda should examine the advantages of quantitative versus qualitative weighting schemes, how best to improve existing methods, and how best to combine those methods (e.g., epidemiology's causal criteria with toxicology's quality criteria).

Sampling and sensitivity analyses tools (SaSAT) for computational modelling

Alexander Hoare — 2008-02-29T02:17:55-00:00

Theoretical Biology and Medical Modelling, Vol. 5 (27 February 2008), 4.

Advanced Biological and Chemical Discovery (ABCD): Centralizing Discovery Knowledge in an Inherently Decentralized World

DK Agrafiotis — 2008-02-27T16:08:10-00:00

J. Chem. Inf. Model., Vol. 47, No. 6. (26 November 2007), pp. 1999-2014.

Abstract: We present ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.C. ABCD is an attempt to bridge multiple continents, data systems, and cultures using modern information technology and to provide scientists with tools that allow them to analyze multifactorial SAR and make informed, data-driven decisions. The system consists of three major components: (1) a data warehouse, which combines data from multiple chemical and pharmacological transactional databases, designed for supreme query performance; (2) a state-of-the-art application suite, which facilitates data upload, retrieval, mining, and reporting, and (3) a workspace, which facilitates collaboration and data sharing by allowing users to share queries, templates, results, and reports across project teams, campuses, and other organizational units. Chemical intelligence, performance, and analytical sophistication lie at the heart of the new system, which was developed entirely in-house. ABCD is used routinely by more than 1000 scientists around the world and is rapidly expanding into other functional areas within the J&J organization.

A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays.

JH Zhang — 2006-01-27T14:00:26-00:00

J Biomol Screen, Vol. 4, No. 2. (1999), pp. 67-73.

The ability to identify active compounds (³hits²) from large chemical libraries accurately and rapidly has been the ultimate goal in developing high-throughput screening (HTS) assays. The ability to identify hits from a particular HTS assay depends largely on the suitability or quality of the assay used in the screening. The criteria or parameters for evaluating the ³suitability² of an HTS assay for hit identification are not well defined and hence it still remains difficult to compare the quality of assays directly. In this report, a screening window coefficient, called ³Z-factor,² is defined. This coefficient is reflective of both the assay signal dynamic range and the data variation associated with the signal measurements, and therefore is suitable for assay quality assessment. The Z-factor is a dimensionless, simple statistical characteristic for each HTS assay. The Z-factor provides a useful tool for comparison and evaluation of the quality of assays, and can be utilized in assay optimization and validation.

Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries.

J Inglese — 2007-08-09T06:09:27-00:00

Proc Natl Acad Sci U S A, Vol. 103, No. 31. (1 August 2006), pp. 11473-11478.

High-throughput screening (HTS) of chemical compounds to identify modulators of molecular targets is a mainstay of pharmaceutical development. Increasingly, HTS is being used to identify chemical probes of gene, pathway, and cell functions, with the ultimate goal of comprehensively delineating relationships between chemical structures and biological activities. Achieving this goal will require methodologies that efficiently generate pharmacological data from the primary screen and reliably profile the range of biological activities associated with large chemical libraries. Traditional HTS, which tests compounds at a single concentration, is not suited to this task, because HTS is burdened by frequent false positives and false negatives and requires extensive follow-up testing. We have developed a paradigm, quantitative HTS (qHTS), tested with the enzyme pyruvate kinase, to generate concentration-response curves for >60,000 compounds in a single experiment. We show that this method is precise, refractory to variations in sample preparation, and identifies compounds with a wide range of activities. Concentration-response curves were classified to rapidly identify pyruvate kinase activators and inhibitors with a variety of potencies and efficacies and elucidate structure-activity relationships directly from the primary screen. Comparison of qHTS with traditional single-concentration HTS revealed a high prevalence of false negatives in the single-point screen. This study demonstrates the feasibility of qHTS for accurately profiling every compound in large chemical libraries (>10(5) compounds). qHTS produces rich data sets that can be immediately mined for reliable biological activities, thereby providing a platform for chemical genomics and accelerating the identification of leads for drug discovery.

Integration of Structure-Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity

P Mazzatorta — 2007-10-25T13:36:07-00:00

J. Chem. Inf. Model., Vol. 47, No. 1. (22 January 2007), pp. 34-38.

Abstract: The Ames mutagenicity test in Salmonella typhimurium is a bacterial short-term in vitro assay aimed at detecting the mutagenicity caused by chemicals. Mutagenicity is considered as an early alert for carcinogenicity. After a number of decades, several (Q)SAR studies on this endpoint yielded enough evidence to make feasible the construction of reliable computational models for prediction of mutagenicity from the molecular structure of chemicals. In this study, we propose a combination of a fragment-based SAR model and an inductive database. The hybrid system was developed using a collection of 4337 chemicals (2401 mutagens and 1936 nonmutagens) and tested using 753 independent compounds (437 mutagens and 316 nonmutagens). The overall error of this system on the external test set compounds is 15% (sensitivity = 15%, specificity = 15%), which is quantitatively similar to the experimental error of Ames test data (average interlaboratory reproducibility determined by the National Toxicology Program). Moreover, each single prediction is provided with a specific confidence level. The results obtained give confidence that this system can be applied to support early and rapid evaluation of the level of mutagenicity concern.

In vitro approaches to develop weight of evidence (WoE) and mode of action (MoA) discussions with positive in vitro genotoxicity results

Dj Kirkland — 2007-08-09T16:20:18-00:00

Mutagenesis, Vol. 22, No. 3. (1 May 2007), pp. 161-175.

A recent analysis by Kirkland et al. [Kirkland, D., Aardema, M., Henderson, L. and Muller, L. (2005) Evaluation of the ability of a battery of 3 in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens. I. Sensitivity, specificity and relative predictivity. Mutat. Res. 584, 1-256] demonstrated an extremely high false positive rate for in vitro genotoxicity tests when compared with carcinogenicity in rodents. In many industries, decisions have to be made on the safety of new substances, and health risk to humans, without rodent carcinogenicity data being available. In such cases, the usual way to determine whether a positive in vitro genotoxicity result is relevant (i.e. indicates a hazard) for humans is to develop weight of evidence (WoE) or mode of action (MoA) arguments. These are based partly on further in vitro investigations, but usually rely heavily on tests for genotoxicity in one or more in vivo assays. However, for certain product types in the European Union, the use of animals for genotoxicity testing (as well as for other endpoints) will be prohibited within the next few years. Many different examples have been described that indicate DNA damage and genotoxic responses in vitro can arise through non-relevant in vitro events that are a result of the test systems and conditions used. The majority of these non-relevant in vitro events can be grouped under a category of overload of normal physiology' that would not be expected to occur in exposed humans. However, obtaining evidence in support of such MoAs is not easy, particularly for those industries prohibited from carrying out in vivo testing. It will become necessary to focus on in vitro studies to provide evidence of non-DNA, threshold or in vitro-specific processes and to discuss the potential for such genotoxic effects to occur in exposed humans. Toward this end, we surveyed the published literature for in vitro approaches that may be followed to determine whether a genotoxic effect observed in vitro will occur in humans. Unfortunately, many of the approaches we found are based on only a few published examples and validated approaches with consensus recommendations often do not exist. This analysis highlights the urgent need for developing consensus approaches that do not rely on animal studies for dealing with in vitro genotoxins. 10.1093/mutage/gem006

ELUCIDATING MECHANISMS OF DRUG-INDUCED TOXICITY

Daniel Liebler — 2007-08-07T13:10:04-00:00

Nat Rev Drug Discov, Vol. 4, No. 5. (May 2005), pp. 410-420.

Local and Global Quantitative Structure-Activity Relationship Modeling and Prediction for the Baseline Toxicity

H Yuan — 2007-07-17T14:19:08-00:00

J. Chem. Inf. Model., Vol. 47, No. 1. (22 January 2007), pp. 159-169.

Abstract: The predictive accuracy of the model is of the most concern for computational chemists in quantitative structure-activity relationship (QSAR) investigations. It is hypothesized that the model based on analogical chemicals will exhibit better predictive performance than that derived from diverse compounds. This paper develops a novel scheme called "clustering first, and then modeling" to build local QSAR models for the subsets resulted from clustering of the training set according to structural similarity. For validation and prediction, the validation set and test set were first classified into the corresponding subsets just as those of the training set, and then the prediction was performed by the relevant local model for each subset. This approach was validated on two independent data sets by local modeling and prediction of the baseline toxicity for the fathead minnow. In this process, hierarchical clustering was employed for cluster analysis, k-nearest neighbor for classification, and partial least squares for the model generation. The statistical results indicated that the predictive performances of the local models based on the subsets were much superior to those of the global model based on the whole training set, which was consistent with the hypothesis. This approach proposed here is promising for extension to QSAR modeling for various physicochemical properties, biological activities, and toxicities.

Structure-based classification of chemical reactions without assignment of reaction centers.

QY Zhang — 2007-07-17T08:44:14-00:00

J Chem Inf Model, Vol. 45, No. 6. (c 2005), pp. 1775-1783.

The automatic classification of chemical reactions is of high importance for the analysis of reaction databases, reaction retrieval, reaction prediction, or synthesis planning. In this work, the classification of photochemical reactions was investigated with no explicit assignment of the reacting centers. Classifications were explored with Random Forests or Kohonen neural networks in three different situations, using different levels of information: (a) pairs of reactants were classified according to the type of reaction they produce, (b) products were classified according to the type of reaction from which they can be synthesized, and (c) reactions were classified from the difference between the descriptors of the product and the descriptors of the reactants. In all cases molecular maps of atom-level properties (MOLMAPs) were used as descriptors. They are generated by a self-organizing map and encode physicochemical properties of the bonds available in a molecule. Correct classification could be achieved for approximately 90% of the 78 reactions in an independent test set.

Genome-Scale Classification of Metabolic Reactions: A Chemoinformatics Approach

Diogo Latino — 2006-07-11T10:27:38-00:00

Angewandte Chemie International Edition, Vol. 45, No. 13. (2006), pp. 2066-2069.

No Abstract

A Novel Logic-Based Approach for Quantitative Toxicology Prediction

A Amini — 2007-07-09T10:51:14-00:00

J. Chem. Inf. Model., Vol. 47, No. 3. (29 May 2007), pp. 998-1006.

Abstract: There is a pressing need for accurate in silico methods to predict the toxicity of molecules that are being introduced into the environment or are being developed into new pharmaceuticals. Predictive toxicology is in the realm of structure activity relationships (SAR), and many approaches have been used to derive such SAR. Previous work has shown that inductive logic programming (ILP) is a powerful approach that circumvents several major difficulties, such as molecular superposition, faced by some other SAR methods. The ILP approach reasons with chemical substructures within a relational framework and yields chemically understandable rules. Here, we report a general new approach, support vector inductive logic programming (SVILP), which extends the essentially qualitative ILP-based SAR to quantitative modeling. First, ILP is used to learn rules, the predictions of which are then used within a novel kernel to derive a support-vector generalization model. For a highly heterogeneous dataset of 576 molecules with known fathead minnow fish toxicity, the cross-validated correlation coefficients (R2CV) from a chemical descriptor method (CHEM) and SVILP are 0.52 and 0.66, respectively. The ILP, CHEM, and SVILP approaches correctly predict 55, 58, and 73%, respectively, of toxic molecules. In a set of 165 unseen molecules, the R2 values from the commercial software TOPKAT and SVILP are 0.26 and 0.57, respectively. In all calculations, SVILP showed significant improvements in comparison with the other methods. The SVILP approach has a major advantage in that it uses ILP automatically and consistently to derive rules, mostly novel, describing fragments that are toxicity alerts. The SVILP is a general machine-learning approach and has the potential of tackling many problems relevant to chemoinformatics including in silico drug design.

A systematic approach to simulating metabolism in computational toxicology. I. The TIMES heuristic modelling framework.

OG Mekenyan — 2007-06-26T10:46:04-00:00

Curr Pharm Des, Vol. 10, No. 11. (2004), pp. 1273-1293.

Designing biologically active chemicals and managing their risks requires a holistic perspective on the chemical-biological interactions that form the basis of selective toxicity. The balance of therapeutic and adverse outcomes for new drugs and pesticides is managed by shaping the probabilities for transport, metabolism, and molecular initiating events. For chemicals activated as well as detoxified by metabolism, selective toxicity may be considered in terms of relative probabilities, which shift dramatically across species as well as within a population, depending on many factors. The complexity in toxicology that results from metabolism has been troublesome in QSAR research because the parent structure is less relevant to predicting ultimate effects and finding reference species/conditions for metabolic rates seems hopeless. Even the complexity of comparative xenobiotic metabolism itself seems paradoxical in light of the evidence of highly conserved catabolic processes across species. Clearly, predicting the role of metabolism in selective toxicity and adverse health outcomes requires a probabilistic framework for deterministic models as well as the many factors shaping the metabolic probability distributions under specific conditions. This paper presents a tissue metabolism simulator (TIMES), which uses a heuristic algorithm to generate plausible metabolic maps from a comprehensive library of biotransformations and abiotic reactions and estimates for system-specific transformation probabilities. The transformation probabilities can be calibrated to specific reference conditions using transformation rate information from systematic testing. In the absence of rate data, a combinatorial algorithm is used to translate known metabolic maps taken from reference systems into best-fit transformation probabilities. Finally, toxicity test data itself can be used to shape the transformation probabilities for toxicity pathways in which the metabolic activation is the rate-limiting process leading to a toxic effect. The conceptual approach for metabolic simulation will be presented along with practical uses in forecasting plausible activated metabolites.

Identification of the Structural Requirements for Mutagencitiy, by Incorporating Molecular Flexibility and Metabolic Activation of Chemicals. II. General Ames Mutagenicity Model

R Serafimova — 2007-06-26T10:23:22-00:00

Chem. Res. Toxicol., Vol. 20, No. 4. (16 April 2007), pp. 662-676.

Abstract: The tissue metabolic simulator (TIMES) modeling approach is a hybrid expert system that couples a metabolic simulator together with structure toxicity rules, underpinned by structural alerts, to predict interaction of chemicals or their metabolites with target macromolecules. Some of the structural alerts representing the reactivity pattern-causing effect could interact directly with the target whereas others necessitated a combination with two- or three-dimensional quantitative structure-activity relationship models describing the firing of the alerts from the rest of the molecules. Recently, TIMES has been used to model bacterial mutagenicity [Mekenyan, O., Dimitrov, S., Serafimova, R., Thompson, E., Kotov, S., Dimitrova, N., and Walker, J. (2004) Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals I: TA100 model. Chem. Res. Toxicol. 17 (6), 753-766]. The original model was derived for a single tester strain, Salmonella typhimurium (TA100), using the Ames test by the National Toxicology Program (NTP). The model correctly identified 82% of the primary acting mutagens, 94% of the nonmutagens, and 77% of the metabolically activated chemicals in a training set. The identified high correlation between activities across different strains changed the initial strategic direction to look at the other strains in the next modeling developments. In this respect, the focus of the present work was to build a general mutagenicity model predicting mutagenicity with respect to any of the Ames tester strains. The use of all reactivity alerts in the model was justified by their interaction mechanisms with DNA, found in the literature. The alerts identified for the current model were analyzed by comparison with other established alerts derived from human experts. In the new model, the original NTP training set with 1341 structures was expanded by 1626 proprietary chemicals provided by BASF AG. Eventually, the training set consisted of 435 chemicals, which are mutagenic as parents, 397 chemicals that are mutagenic after S9 metabolic activation, and 2012 nonmutagenic chemicals. The general mutagenicity model was found to have 82% sensitivity, 89% specificity, and 88% concordance for training set chemicals. The model applicability domain was introduced accounting for similarity (structural, mechanistic, etc.) between predicted chemicals and training set chemicals for which the model performs correctly.

International Science Forum on Computational Toxicology

Jin — 2007-06-23T11:49:13-00:00

(21-23 May 2007)

Chemoinformatics: past, present, and future.

WL Chen — 2007-06-15T13:24:02-00:00

J Chem Inf Model, Vol. 46, No. 6. (c 2006), pp. 2230-2255.

The history of chemoinformatics is reviewed in a decade-by-decade manner from the 1940s to the present. The focus is placed on four traditional research areas: chemical database systems, computer-assisted structure elucidation systems, computer-assisted synthesis design systems, and 3D structure builders. Considering the fact that computer technology has been one of the major driving forces of the development of chemoinformatics, each section will start from a brief description of the new advances in computer technology of each decade. The summary and future prospects are given in the last section.

Basic overview of chemoinformatics.

T Engel — 2007-02-05T05:18:07-00:00

J Chem Inf Model, Vol. 46, No. 6. (c 2006), pp. 2267-2277.

There is no particular point in time that determines when chemoinformatics was founded or established. It slowly evolved from several, often quite humble beginnings. Scientists in various fields of chemistry struggled with the development of computer methods which allowed them to manage the enormous amount of chemical information and to find relationships between the structure and properties of a compound. During the 1960s some early developments appeared that led to a flurry of activities in the 1970s. This review provides a general overview of basic methods in the specific fields of chemoinformatics, from encoding chemical compounds, storing and searching data in databases, to generating and analyzing these data. In addition, the chief interconnecting points of chemoinformatics applications are highlighted including the contributions of Johann Gasteiger to this field.

Recent Advances in Chemoinformatics.

Dimitris Agrafiotis — 2007-06-05T20:16:52-00:00

J Chem Inf Model (19 May 2007)

Chemoinformatics is a large scientific discipline that deals with the storage, organization, management, retrieval, analysis, dissemination, visualization, and use of chemical information. Chemoinformatics techniques are used extensively in drug discovery and development. Although many consider it a mature field, the advent of high-throughput experimental techniques and the need to analyze very large data sets have brought new life and challenges to it. Here, we review a selection of papers published in 2006 that caught our attention with regard to the novelty of the methodology that was presented. The field is seeing significant growth, which will be further catalyzed by the widespread availability of public databases to support the development and validation of new approaches.

Chemical structure indexing of toxicity data on the internet: moving toward a flat world.

AM Richard — 2007-06-15T13:13:05-00:00

Curr Opin Drug Discov Devel, Vol. 9, No. 3. (May 2006), pp. 314-325.

Standardized chemical structure annotation of public toxicity databases and information resources is playing an increasingly important role in the 'flattening' and integration of diverse sets of biological activity data on the Internet. This review discusses public initiatives that are accelerating the pace of this transformation, with particular reference to toxicology-related chemical information. Chemical content annotators, structure locator services, large structure/data aggregator web sites, structure browsers, International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) codes, toxicity data models and public chemical/biological activity profiling initiatives are all playing a role in overcoming barriers to the integration of toxicity data, and are bringing researchers closer to the reality of a mineable chemical Semantic Web. An example of this integration of data is provided by the collaboration among researchers involved with the Distributed Structure-Searchable Toxicity (DSSTox) project, the Carcinogenic Potency Project, projects at the National Cancer Institute and the PubChem database.

The Expanding Role of Predictive Toxicology: An Update on the (Q)SAR Models for Mutagens and Carcinogens.

R Benigni — 2007-06-15T13:10:40-00:00

J Environ Sci Health C Environ Carcinog Ecotoxicol Rev, Vol. 25, No. 1. (January 2007), pp. 53-97.

Different regulatory schemes worldwide, and in particular, the preparation for the new REACH (Registration, Evaluation and Authorization of CHemicals) legislation in Europe, increase the reliance on estimation methods for predicting potential chemical hazard. To meet the increased expectations, the availability of valid (Q)SARs becomes a critical issue, especially for endpoints that have complex mechanisms of action, are time-and cost-consuming, and require a large number of animals to test. Here, findings from the survey on (Q)SARs for mutagenicity and carcinogenicity, initiated by the European Chemicals Bureau (ECB) and carried out by the Istituto Superiore di Sanita' are summarized, key aspects are discussed, and a broader view towards future needs and perspectives is given.

Navigating chemical space for biology and medicine

Christopher Lipinski — 2004-12-16T13:06:15-00:00

Nature, Vol. 432, No. 7019. (16 December 2004), pp. 855-861.

Practical feature subset selection for Machine Learning

M Hall — 2007-06-07T10:33:03-00:00

(1996)

Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selectors are algorithms that attempt to identify and remove as much...

Strategy for genotoxicity testing--metabolic considerations.

WW Ku — 2007-06-07T09:44:02-00:00

Mutat Res, Vol. 627, No. 1. (3 February 2007), pp. 59-77.

The report from the 2002 International Workshop on Genotoxicity Tests (IWGT) Strategy Expert Group emphasized metabolic considerations as an important area to address in developing a common strategy for genotoxicity testing. A working group convened at the 2005 4th IWGT to discuss this area further and propose practical strategy recommendations. To propose a strategy, the working group reviewed: (1) the current status and deficiencies, including examples of carcinogens "missed" in genotoxicity testing, established shortcomings of the standard in vitro induced S9 activation system and drug metabolite case examples; (2) the current status of possible remedies, including alternative S9 sources, other external metabolism systems or genetically engineered test systems; (3) any existing positions or guidance. The working group established consensus principles to guide strategy development. Thus, a human metabolite of interest should be represented in genotoxicity and carcinogenicity testing, including evaluation of alternative genotoxicity in vitro metabolic activation or test systems, and the selection of a carcinogenicity test species showing appropriate biotransformation. Appropriate action triggers need to be defined based on the extent of human exposure, considering any structural knowledge of the metabolite, and when genotoxicity is observed upon in vitro testing in the presence of metabolic activation. These triggers also need to be considered in defining the timing of human pharmaceutical ADME assessments. The working group proposed two strategies to consider; a more proactive approach, which emphasizes early metabolism predictions to drive appropriate hazard assessment; and a retroactive approach to manage safety risks of a unique or "major" metabolite once identified and quantitated from human clinical ADME studies. In both strategies, the assessment of the genotoxic potential of a metabolite could include the use of an alternative or optimized in vitro metabolic activation system, or direct testing of an isolated or synthesized metabolite. The working group also identified specific areas where more data or experiences need to be gained to reach consensus. These included defining a discrete exposure action trigger for safety assessment and when direct testing of a metabolite of interest is warranted versus the use of an alternative in vitro activation system, a universal recommendation for the timing of human ADME studies for drug candidates and the positioning of metabolite structural knowledge (through in silico systems, literature, expert analysis) in supporting metabolite safety qualification. Lastly, the working group outlined future considerations for refining the initially proposed strategies. These included the need for further evaluation of the current in vitro genotoxicity testing protocols that can potentially perturb or reduce the level of metabolic activity (potential alterations in metabolism associated with both the use of some solvents to solubilize test chemicals and testing to the guidance limit dose), and proposing broader evaluations of alternative metabolic activation sources or engineered test systems to further challenge the suitability of (or replace) the current induced liver S9 activation source.

A Comparative Study of Machine Learning Algorithms Applied to Predictive Toxicology Data Mining

Daniel Neagu — 2007-06-07T09:33:34-00:00

Alternative Laboratory Animals, No. 35. (2007), pp. 25-32.

Computational Prediction of the Chromosome-Damaging Potential of Chemicals

Andreas Rothfuss — 2007-06-05T11:47:15-00:00

We report on the generation of computer-based models for the prediction of the chromosome-damaging potential of chemicals as assessed in the in Vitro chromosome aberration (CA) test. On the basis of publicly available CA-test results of more than 650 chemical substances, half of which are drug-like compounds, we generated two different computational models. The first model was realized using the (Q)SAR tool MCASE. Results obtained with this model indicate a limited performance (53%) for the assessment of a chromosome-damaging potential (sensitivity), whereas CA-test negative compounds were correctly predicted with a specificity of 75%. The low sensitivity of this model might be explained by the fact that the underlying 2D-structural descriptors only describe part of the molecular mechanism leading to the induction of chromosome aberrations, that is, direct drug-DNA interactions. The second model was constructed with a more sophisticated machine learning approach and generated a classification model based on 14 molecular descriptors, which were obtained after feature selection. The performance of this model was superior to the MCASE model, primarily because of an improved sensitivity, suggesting that the more complex molecular descriptors in combination with statistical learning approaches are better suited to model the complex nature of mechanisms leading to a positive effect in the CA-test. An analysis of misclassified pharmaceuticals by this model showed that a large part of the false-negative predicted compounds were uniquely positive in the CA-test but lacked a genotoxic potential in other mutagenicity tests of the regulatory testing battery, suggesting that biologically nonsignificant mechanisms could be responsible for the observed positive CA-test result. Since such mechanisms are not amenable to modeling approaches it is suggested that a positive prediction made by the model reflects a biologically significant genotoxic potential. An integration of the machine-learning model as a screening tool in early discovery phases of drug development is proposed.d be responsible for the observed positive CA-test result. Since such mechanisms are not amenable to modeling approaches it is suggested that a positive prediction...

Future of Toxicology-Metabolic Activation and Drug Design: Challenges and Opportunities in Chemical Toxicology

TA Baillie — 2007-06-05T11:27:09-00:00

Chem. Res. Toxicol., Vol. 19, No. 7. (17 July 2006), pp. 889-893.

Abstract: The issue of chemically reactive drug metabolites is one of growing concern in the pharmaceutical industry inasmuch as some, but not all, reactive intermediates are believed to play a role as mediators of drug-induced toxicities. While it is now relatively straightforward to identify these short-lived electrophilic species through appropriate in vitro "trapping" experiments, our current understanding of mechanistic aspects of xenobiotic-induced toxicities is such that we cannot predict which reactive intermediates are likely to cause a toxic insult and which will be benign. Little is known about the identities of the macromolecular targets (primarily proteins) of these electrophiles or the functional consequences of their covalent modification by reactive drug metabolites. As a result, several companies have adopted approaches to minimize the potential for metabolic activation of drug candidates at the discovery/lead optimization phase as a default strategy. However, research leading to a deeper insight into mechanistic aspects of toxicities caused by reactive drug metabolites will aid greatly in the rational design of drug candidates with superior safety profiles and represents a challenging and exciting opportunity for chemical toxicology.

ADMET in silico modelling: towards prediction paradise?

H van de Waterbeemd — 2006-05-02T16:43:02-00:00

Nat Rev Drug Discov, Vol. 2, No. 3. (March 2003), pp. 192-204.

Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.

SLIPPER-2001 - Software for Predicting Molecular Properties on the Basis of Physicochemical Descriptors and Structural Similarity

OA Raevsky — 2007-05-29T15:02:23-00:00

J. Chem. Inf. Model., Vol. 42, No. 3. (28 May 2002), pp. 540-549.

Abstract: A new approach for predicting the lipophilicity (log P), solubility (log Sw), and oral absorption of drugs in humans (FA) is described. It is based on structural and physicochemical similarity and is realized in the software program SLIPPER-2001. Calculated and experimental values of log P, log Sw, and FA for 42 drugs were used to demonstrate the predictive power of the program. Reliable results were obtained for simple compounds, for complex chemicals, and for drugs. Thus, the principle of "similar compounds display similar properties" together with estimating incremental changes in properties by using differences in physicochemical parameters results in "structure - property " predictive models even in the absence of a precise understanding of the mechanisms involved.

History of the enzyme nomenclature system

Keith Tipton — 2007-05-14T09:38:06-00:00

Bioinformatics, Vol. 16, No. 1. (1 January 2000), pp. 34-40.

Naming things is essential for people to understand one another, no matter what language or field of interest is involved. This is as true for enzymes, genes and chemicals as it is for birds, food, flowers, etc. Effective communication requires a lack of ambiguity, but, in practice, ambiguities abound even between people who use the same language in different parts of the world, or even within the same country. Whereas ambiguities in the words used for common objects or actions have been the basis for many, more-or-less memorable jokes, they can also cause a great deal of confusion. Such linguistic chaos is welcomed by many as being a part of a diverse heritage that should be preserved at all costs to prevent us from descending into Orwellian newspeak'. However, in the sciences, there are distinct advantages in others being able to understand what one is doing. Many groups have stressed the need for standardized, universally accepted systems of nomenclature in chemistry, genetics, enzymology, etc. However, it is the universal acceptance that usually causes the problem. It is rare to find people who will admit that they find nomenclature to be an interesting subject, but many who profess contempt for it will get very excited if it is suggested that their pet nomenclature should be changed in the interest of clarity or uniformity. This account will consider the development of the enzyme nomenclature system, its benefits, shortcomings and future prospects. Contact: ktipton@tcd.ie 10.1093/bioinformatics/16.1.34

Prediction of protein solvent accessibility using fuzzy <it>k</it>-nearest neighbor method

Jaehyun Sim — 2005-06-11T19:56:05-00:00

Bioinformatics, Vol. 21, No. 12. (15 June 2005), pp. 2844-2849.

Interactive exploration of fuzzy clusters using Neighborgrams

Michael Berthold — 2007-04-30T08:49:34-00:00

Fuzzy Sets and Systems, Vol. 149 (2005), pp. 21-37.

We describe an interactive method to generate a set of fuzzy clusters for classes of interest of a given, labeled data set. The presented method is therefore best suited for applications where the focus of analysis lies on a model for the minority class or for small to medium-sized data sets. The clustering algorithm creates one–dimensional models of the neighborhood for a set of patterns by constructing cluster candidates for each pattern of interest and then chooses the best subset of clusters that form a global model of the data. The accompanying visualization of these neighborhoods allows the user to interact with the clustering process by selecting, discarding, or fine–tuning potential cluster candidates. Clusters can be crisp or fuzzy and the latter leads to a substantial improvement of the classification accuracy.We demonstrate the performance of the underlying algorithm on several data sets from the StatLog project and show its usefulness for visual cluster exploration on the Iris data and a large molecular dataset from the National Cancer Institute.

The random subspace method for constructing decision forests

Tin Ho — 2007-04-13T09:54:51-00:00

Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 20, No. 8. (1998), pp. 832-844.

Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces. The subspace method is compared to single-tree classifiers and other forest construction methods by experiments on publicly available datasets, where the method's superiority is demonstrated. We also discuss independence between trees in a forest and relate that to the combined classification accuracy

Ensembling local learners ThroughMultimodal perturbation

Zhi-Hua Zhou — 2007-04-13T09:49:06-00:00

Systems, Man and Cybernetics, Part B, IEEE Transactions on, Vol. 35, No. 4. (2005), pp. 725-735.

Ensemble learning algorithms train multiple component learners and then combine their predictions. In order to generate a strong ensemble, the component learners should be with high accuracy as well as high diversity. A popularly used scheme in generating accurate but diverse component learners is to perturb the training data with resampling methods, such as the bootstrap sampling used in bagging. However, such a scheme is not very effective on local learners such as nearest-neighbor classifiers because a slight change in training data can hardly result in local learners with big differences. In this paper, a new ensemble algorithm named Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR) is proposed for building ensembles of local learners, which utilizes multimodal perturbation to help generate accurate but diverse component learners. In detail, FASBIR employs the perturbation on the training data with bootstrap sampling, the perturbation on the input attributes with attribute filtering and attribute subspace selection, and the perturbation on the learning parameters with randomly configured distance metrics. A large empirical study shows that FASBIR is effective in building ensembles of nearest-neighbor classifiers, whose performance is better than that of many other ensemble algorithms.

QSAR - How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets

Peter Gedeck — 2007-01-25T16:03:18-00:00

J. Chem. Inf. Model., Vol. 46, No. 5. (2006), pp. 1924-1936.

Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity

Neal Cariello — 2006-12-18T16:32:29-00:00

Mutagenesis, Vol. 17, No. 4. (1 July 2002), pp. 321-329.

The performance of two computer programs, DEREK and TOPKAT, was examined with regard to predicting the outcome of the Ames bacterial mutagenicity assay. The results of over 400 Ames tests conducted at Glaxo Wellcome (now GlaxoSmithKline) during the last 15 years on a wide variety of chemical classes were compared with the mutagenicity predictions of both computer programs. DEREK was considered concordant with the Ames assay if (i) the Ames assay was negative (not mutagenic) and no structural alerts for mutagenicity were identified or (ii) the Ames assay was positive (mutagenic) and at least one structural alert was identified. Conversely, the DEREK output was considered discordant if (i) the Ames assay was negative and any structural alert was identified or (ii) the Ames assay was positive and no structural alert was identified. The overall concordance of the DEREK program with the Ames results was 65% and the overall discordance was 35%, based on over 400 compounds. About 23% of the test molecules were outside the permissible limits of the optimum prediction space of TOPKAT. Another 4% of the compounds were either not processable or had indeterminate mutagenicity predictions; these molecules were excluded from the TOPKAT analysis. If the TOPKAT probability was (i) [>=]0.7 the molecule was predicted to be mutagenic, (ii) [<=]0.3 the compound was predicted to be non-mutagenic and (iii) between 0.3 and 0.7 the prediction was considered indeterminate. From over 300 acceptable predictions, the overall TOPKAT concordance was 73% and the overall discordance was 27%. While the overall concordance of the TOPKAT program was higher than DEREK, TOPKAT fared more poorly than DEREK in the critical Ames-positive category, where 60% of the compounds were incorrectly predicted by TOPKAT as negative but were mutagenic in the Ames test. For DEREK, 54% of the Ames-positive molecules had no structural alerts and were predicted to be non-mutagenic. Alternative methods of analyzing the output of the programs to increase the accuracy with Ames-positive compounds are discussed. 10.1093/mutage/17.4.321

Future of Toxicology-Predictive Toxicology: An Expanded View of "Chemical Toxicity"

AM Richard — 2006-12-12T16:51:29-00:00

Chem. Res. Toxicol., Vol. 19, No. 10. (16 October 2006), pp. 1257-1262.

Linking data to models: data regression

Khuloud Jaqaman — 2006-10-24T00:20:15-00:00

Nature Reviews Molecular Cell Biology, Vol. 7, No. 11. (27 September 2006), pp. 813-819.

Multiobjective Optimization in Quantitative Structure-Activity Relationships: Deriving Accurate and Interpretable QSARs

O Nicolotti — 2006-11-30T11:08:57-00:00

J. Med. Chem., Vol. 45, No. 23. (7 November 2002), pp. 5069-5080.

A Review of: Predictive Toxicology

Greene — 2006-08-31T20:12:33-00:00

International Journal of Toxicology, Vol. 25, No. 5. (October 2006), pp. 429-431.

The Distinction between Genotoxic and Epigenetic Carcinogens and Implication for Cancer Risk

John Weisburger — 2006-11-28T16:54:59-00:00

Toxicol. Sci., Vol. 57, No. 1. (1 September 2000), pp. 4-5.

10.1093/toxsci/57.1.4

Artificial Intelligence and Data Mining for Toxicity Prediction

Helma — 2006-05-30T13:54:07-00:00

Current Computer - Aided Drug Design, Vol. 2, No. 2. (June 2006), pp. 123-133.

The Art of Data Mining the Minefields of Toxicity Databases to Link Chemistry to Biology

Yang — 2006-05-30T13:54:07-00:00

Current Computer - Aided Drug Design, Vol. 2, No. 2. (June 2006), pp. 135-150.

Data mining Tutorial (SQL server 2005)

Seth Paul — 2006-08-22T22:48:43-00:00